I've been using TensorFlow and Rust for a while now, and I'm very happy with this binding. Since this crate implements many APIs other than inference, I wanted to have Eager API as well. As far as I know, cppflow has partial support for the Eager API op, so I think I can make a basic implementation by referring to that and tensorflow's eager/c_api_test.
I think there are three major tasks required for this implementation.
* [ ] Creating bindings in `tensorflow-sys`.
* [ ] Implementing the Eager API
* [ ] Implementation of raw op (refer to cppflow)
At present, I believe that I can implement an API that can handle both Tensor and TensorHandle relatively transparently by leaving the Tensor struct as it is and implementing a common trait between Tensor and the newly implemented TensorHandle.
I myself often use TensorFlow for image-related tasks, and the Eager
API will be useful for basic I/O without relying on image crate.
If you have any suggestions or comments, I would appreciate your feedback.